{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PHydro-cover-small.png\">\n",
    "*This is the Jupyter notebook version of the [Python in Hydrology](http://www.greenteapress.com/pythonhydro/pythonhydro.html) by Sat Kumar Tomer.*\n",
    "*Source code is available at [code.google.com](https://code.google.com/archive/p/python-in-hydrology/source).*\n",
    "\n",
    "*The book is available under the [GNU Free Documentation License](http://www.gnu.org/copyleft/fdl.html). If you have comments, corrections or suggestions, please send email to satkumartomer@gmail.com.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Introduction](04.01-Introduction.ipynb) | [Contents](Index.ipynb) | [Precipitation](04.03-Precipitation.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 水蒸气\n",
    "\n",
    "大致上，饱和蒸气压（$e_s$）与空气温度（$T$）通过以下等式相关。\n",
    "\n",
    "<center>$e_s = 611exp(\\frac{17.27T}{237.3+T})$, (4.1) </center>     \n",
    "\n",
    "其中，$e_s$是帕斯卡,$T$为摄氏度。让我们计算$T＝50$时，$e_s$的值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12340.799081\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "T = 50\n",
    "es = 611*np.exp(17.27*T/(237.3+T))\n",
    "print(es)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们在-100≤T≤100的范围内绘制$e_s$与$T$的变化曲线。`plt.plot(x，y)`绘制y与x的线图，默认颜色为蓝色。`plt.xlabel()`和`plt.ylabel()`分别用于x轴和y轴上写标签。`xlable`和`ylabel`的输入必须是一个字符串，或一个包含字符串的变量。`plt.show()`显示图形在电脑屏幕上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yyXL3BWEumScAjVYpIpJkj5duJD83i4+cld43cibq6DubAWb2V+29dvdfJScsEZGeaX99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\nJOmUbEREJOmUbEREJOn+P4NlIZcWxGYHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x59a3126a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "T = np.linspace(-100,100,50)\n",
    "es = 611*np.exp(17.27*T/(237.3+T))\n",
    "plt.plot(T,es)\n",
    "plt.xlabel('T (degree Celcius)')\n",
    "plt.ylabel('es (Pa)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果如上图所示。这个例子演示了如何用图形直观地显示一个变量相对于另一个变量的变化，而前者是后面的显式函数。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
